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The practical test is not whether an algorithm can name a single “best” building. It is whether the team can see the consequences of competing objectives—such as daylight, operational energy and embodied carbon—early enough to make an informed choice. The people who set the brief, interpret the evidence and accept responsibility for the design remain in control.
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What “flipping the design paradigm” means
Conventional building design often develops a preferred concept first and tests performance later. Energy, daylight, carbon and cost analysis may arrive after the plan, structure and façade have acquired momentum. At that point, a poor result can require redesign, compromise or an expensive change order.
The flipped approach starts by defining the outcomes that matter, representing them in a computational model and exploring a broad design space while the project is still flexible. Architecture, structural engineering and building-services engineering are considered together rather than passed from one discipline to another in sequence. The team receives evidence about multiple options, not a late verdict on one option.
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The phrase comes from a comment article by Diego Padilla-Philipps published by Building on 23 May 2024. Its available opening argues that AI could help teams design and build a better-performing building from the outset, potentially improving value and performance while reducing cost. It contrasts fictional, human-like super-intelligence with the narrower AI already embedded in everyday systems. The article’s full text was not available for verification, so claims about its detailed argument should not be extended beyond that opening.
A later account of Padilla-Philipps’s JCT Povey Lecture, published by the Joint Contracts Tribunal on 17 November 2025, gives the approach a clearer working name: “Co-Design”. It combines architecture, engineering and building services with performance-led data modelling and automation, including AI tools. The purpose is to give clients informed choices early while keeping humans at the centre of decisions.
Augmented intelligence is deliberately human-centred
In this context, augmented intelligence means using machines to extend professional capability. Software can generate alternatives, run simulations, identify patterns and expose relationships that would be difficult to examine manually. It cannot set a project’s values, understand every social or contractual implication, or take professional responsibility for the outcome.
At the lecture reported by the JCT, Padilla-Philipps—identified as WSP’s director of Decarbonisation and Innovation in Building Structures—said:
“When we think about automation and AI, we need to think about upskilling, not deskilling. So, thinking about AI as a tool that can help us enhance our intelligence, which is why today’s talk is called ‘Augmented Intelligence’. It’s about people, and it’s about augmenting what we can do to deliver better projects.”
He also described the intended position of the professional:
“It is about putting the engineer, the designer, at the centre with access to all these tools so that we can, through analysis, through simulation, through early-stage valuation of buildings, really augment our intelligence and really transform how buildings are designed.”
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That distinction changes how a team should procure and use the technology. The objective is not to remove architects, engineers or client representatives from the process. It is to give them earlier, clearer evidence and more time to exercise judgement.
What the people on the team still decide
- Clients and stakeholders define priorities, acceptable risk, budget boundaries, programme requirements and the outcomes that justify investment.
- Architects translate requirements into spatial, contextual and experiential propositions and decide which generated options are worth developing.
- Engineers define credible assumptions, check the physics and code implications, and judge whether a modelled result is buildable.
- Cost, programme and construction specialists test whether an attractive performance result can be delivered, procured and maintained.
- The whole team decides how to handle trade-offs and records why an option was selected.
Automation can accelerate these activities, but it does not transfer accountability to a software output.
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How an augmented-intelligence workflow operates
A useful workflow is a sequence of controlled decisions rather than a “press generate” exercise. The exact software stack will vary, but the logic is consistent.
1. Define the brief as measurable objectives
Start by converting the client’s ambitions into objectives the model can evaluate. A brief might include spatial daylight autonomy, energy-use intensity, embodied carbon, floor area, comfort, structural spans, planning limits and a target completion date. Some requirements are hard constraints—such as a site boundary or a minimum floor area. Others are preferences that can be traded against one another.
Write down the units, calculation method, baseline and acceptable range for every metric. “Low carbon” is not a usable objective until the team specifies whether it means upfront embodied carbon, whole-life carbon, a particular life-cycle boundary or a comparison with a named baseline.
2. Build a parametric representation
A parametric model describes the variables that may change and the rules that keep each option coherent. Variables might include floor-plate dimensions, orientation, window-to-wall ratio, shading depth, structural grid, core position, glazing properties, floor-to-floor height or plant strategy. The model should also enforce project constraints so that the system does not spend time generating options that cannot be used.
Keeping assumptions explicit is essential. A model that silently fixes occupancy, schedules, weather data, material quantities or equipment efficiencies can produce precise-looking but misleading comparisons.
3. Connect the model to simulation
Each generated design is passed through the relevant analysis tools. Depending on the brief, that may include daylight, thermal, energy, structural, carbon, acoustic or cost calculations. The outputs become comparable indicators attached to the same design option.
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Simulation is not a measurement of the finished building. It is a conditional prediction based on the inputs and methods selected. The team should therefore preserve the model version, input data, weather file, material factors and calculation settings used for every result.
4. Use automation or machine learning to search the design space
Automation can vary parameters and run batches of simulations. Machine-learning techniques can help identify promising regions of the design space or approximate expensive calculations after enough examples have been generated. The point is to examine more alternatives than a team could test manually, not to make the model’s assumptions disappear.
Search methods should be chosen to match the decision. A single-objective optimisation can be appropriate when one requirement dominates. Most building decisions are multi-objective: improving daylight may increase cooling demand or material quantities, while reducing glazing can affect views and user experience. A multi-objective method preserves those tensions instead of hiding them behind one arbitrary score.
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5. Produce a catalogue of options
Padilla-Philipps’s January 2025 Building article describes Daisy as using parametric design, machine learning and multi-objective optimisation to produce a catalogue of potential building designs. Each option is accompanied by simulation results, allowing a team to compare indicators such as embodied carbon and energy-use intensity.
A catalogue is more useful than a single recommendation because it makes the consequences of different priorities visible. The team can identify a group of non-dominated options—designs for which improving one objective would worsen another—and discuss those choices with the client.
6. Review the evidence with the multidisciplinary team
Designers and engineers should examine the geometry and the assumptions behind an attractive result. A low-energy option may rely on a façade that is difficult to procure. A low-carbon option may require a structural system that conflicts with the planning concept. A daylight result may be sensitive to neighbouring buildings that were not represented in the model.
This review is where professional knowledge turns computation into a project decision. The team can reject an option, revise the constraints, add a missing objective or commission a more detailed analysis.
7. Validate before committing
Shortlisted options need progressively higher-fidelity checks. Re-run key analyses with updated geometry, confirmed material quantities, realistic operating assumptions and the project’s applicable standards. Keep a clear distinction between an early-stage screening result and a design suitable for approval, procurement or construction.
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A peer-reviewed paper in Proceedings of the Institution of Civil Engineers: Engineering and Computational Mechanics (2023) presents Daisy as a flexible, performance-based parametric tool for designer-driven, multi-objective optimisation. The reported demonstration concerns a 63-storey commercial building and examines how a design space can be analysed across architectural and engineering objectives at an early stage.
The paper compares simulated designs using spatial daylight autonomy, energy-use intensity and embodied carbon. Its figures are valuable because they show both the potential of early exploration and the cost of treating one metric as the whole definition of performance.
| Reported design move | Position within the simulated design set | What it illustrates |
|---|---|---|
| 14.7% increase in spatial daylight autonomy | The benchmark moved from the 10% of models with the highest energy-use intensity to lower energy-use intensity than 97% of the other simulated designs; its embodied carbon was higher than 90% of the models. | A daylight-oriented improvement can be associated with a substantial embodied-carbon penalty, even when energy-use intensity improves relative to the benchmark. |
| 32.8% decrease in spatial daylight autonomy | Energy-use intensity was below 75% and embodied carbon below 60% of the simulated designs; the paper reports 4.4% lower energy-use intensity and 1.3% lower embodied carbon. | Accepting less daylight in the tested scenario produced modest reductions in two other indicators. |
These numbers come from one simulated 63-storey commercial-building case study. They are comparisons within that design set, not sector-wide benchmarks, guaranteed savings or proof that one option is right for every client. The result depends on the objectives, constraints, baseline and simulation assumptions selected by the project team.
Why “optimal” never means universally best
An optimisation model can find the best result for the objectives and constraints it has been given. It cannot establish a universally optimal building because buildings serve different occupants, sites, climates, regulations, budgets and delivery strategies.
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Suppose a client prioritises visual comfort and a strong connection to the outside. A design with higher spatial daylight autonomy may be preferable even if it carries additional embodied carbon. Another client may have a strict upfront-carbon limit and accept a different daylight profile. Neither decision is an error if the priorities are explicit and the consequences are understood.
Teams should resist reducing several indicators to an unexplained composite score. If a weighted score is used, record who selected the weights, what alternatives were tested and how the ranking changes when the weights change. Showing a Pareto front or a clearly labelled comparison table usually preserves more decision information than a single “best” label.
A practical comparison framework for design options
When two or more options are presented to a client, compare them on the same basis. At minimum, use the three indicators demonstrated in the Daisy case study and add the project’s own requirements and constraints.
| Criterion | Questions to ask | Evidence to show |
|---|---|---|
| Spatial daylight autonomy | What percentage of occupied floor area meets the stated daylight threshold, and under which occupancy and weather assumptions? | Metric definition, threshold, simulation period and floor-area map. |
| Energy-use intensity | Is the value modelled site energy, source energy or another definition? Which systems and operating schedules are included? | Units, end-use breakdown, schedules, weather file and baseline. |
| Embodied carbon | Which life-cycle stages, materials and replacement assumptions are counted? | Carbon factors, quantities, system boundary and data date. |
| Client requirements | Does the option meet floor area, views, flexibility, comfort, planning and budget needs? | Requirement-by-requirement compliance notes and unresolved issues. |
| Model constraints | Which site, structural, façade, services, code and procurement limits were represented? | Constraint list, exclusions and sensitivity checks. |
Present the assumptions beside the results, not in a separate appendix that changes how the numbers are interpreted. If one option was tested with a different baseline or a different level of model detail, label the comparison as provisional.
How a design practice can introduce the method responsibly
Start with a bounded pilot
Choose a project stage where decisions are still reversible and define a small set of questions. A pilot might compare massing, orientation and façade ratios against daylight, energy and embodied-carbon indicators. Avoid promising a complete automated design solution before the team has established reliable data and review procedures.
Assign ownership for every input
Name the person responsible for each assumption: occupancy, weather, material factors, structural rules, services efficiencies and cost data. Record the date and source of each input. When an assumption changes, the team should be able to identify which options need to be re-run.
Make model literacy part of professional development
Architects and engineers need enough understanding to interrogate a model, recognise a boundary condition and explain uncertainty to a client. Upskilling should cover parameter definitions, sensitivity, validation, data quality and the limits of machine-learning approximations. Training is not an optional add-on; it is how the organisation avoids deskilling people into passive acceptance of software outputs.
Use a decision log
For each major choice, record the options considered, the metrics shown, the assumptions that mattered, the people who reviewed the result and the reason for the decision. This creates an audit trail that remains useful when the design changes or a project team member leaves.
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An automated screening result should not by itself approve a life-safety system, structural concept, compliance submission or construction change. Require discipline leads to review those decisions and specify the additional analysis or site information needed before approval.
Common failure modes and how to correct them
Optimising the wrong objective
If the model rewards a metric that is easy to calculate but weakly related to the client’s actual goal, it can produce a technically impressive irrelevance. Revisit the brief and add the missing outcome rather than tuning the algorithm to defend the first result.
Confusing model precision with real-world certainty
A result reported to several decimal places is not necessarily more reliable. Test the sensitivity of rankings to weather, occupancy, material factors and geometry. Show ranges or confidence limits where they are available, and label early estimates as such.
Leaving buildability outside the model
A design can perform well in simulation but fail on access, tolerances, sequencing, procurement or maintenance. Include construction and facilities expertise in the option review, and carry a buildability check into the shortlist rather than waiting for detailed design.
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A combined score can make a daylight penalty or carbon increase invisible. Display the underlying indicators and explain any weighting. If stakeholders disagree about the preferred option, that disagreement may reflect different values rather than a technical failure.
Generating options without a stopping rule
More alternatives do not automatically create a better decision. Stop when additional runs no longer change the viable set, when the uncertainties dominate the differences, or when the team has enough evidence to choose a direction. The stopping rule should be agreed before results are used to justify a preferred design.
Assuming AI replaces coordination
Automation can expose conflicts earlier, but it does not resolve responsibility between disciplines. Keep coordination meetings, design reviews and client conversations in the workflow. Use the catalogue to improve those conversations, not to bypass them.
What this means for clients, designers and contractors
For clients
Ask to see the objectives, assumptions and alternatives, not only the selected rendering. Clarify which outcomes are mandatory and which can be traded. Ask how sensitive the recommendation is to changes in energy prices, occupancy, material data or planning constraints. Early evidence is most valuable when it improves the brief before the project is locked into a single concept.
For architects and engineers
Keep authorship of the design intent while using computation to widen the search. Challenge options that appear optimal for reasons the team cannot explain. Ensure that a promising geometry is translated into a coordinated architectural, structural and services proposal before it is presented as a project solution.
For contractors and operators
Bring construction sequencing, procurement, commissioning and maintenance knowledge into the evaluation. A lower modelled energy-use intensity is only useful if the systems can be installed, balanced, commissioned and operated as assumed. Feedback from delivery and operation can improve the parameters used on later projects.
The evidence-based case for changing the sequence
The strongest argument for augmented intelligence is timing. Performance analysis conducted while massing, orientation, structure and façade strategy are still fluid can influence the design at relatively low cost. The Daisy case study demonstrates that materially different positions in a simulated design set can emerge from changes in objectives, and that daylight, energy and embodied carbon do not automatically move together.
That evidence supports a disciplined change in sequence: define outcomes, explore alternatives, understand trade-offs, then develop the chosen direction. It does not support handing design authority to an opaque system or claiming that an algorithm has discovered a universally superior building.
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Used properly, the technology makes professional judgement more consequential, not less. Designers and engineers see more of the consequences of their choices, clients receive clearer options earlier, and the project team can explain why a direction was selected. That is the practical meaning of flipping the design paradigm upside down.
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